Language patterns after large-scale upheavals - FAQs

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How do you define mass shootings?

Our data source, The Violence Project, defines a public mass shooting as follows:

“a multiple homicide incident in which four or more victims are murdered with firearms—not including the offender(s)—within one event, and at least some of the murders occurred in a public location or locations in close geographical proximity (e.g., a workplace, school, restaurant, or other public settings), and the murders are not attributable to any other underlying criminal activity or commonplace circumstance (armed robbery, criminal competition, insurance fraud, argument, or romantic triangle).”

Which mass shootings did you include?

We included all 60 mass shootings in the United States from April 2013 to March 2023 (The Violence Project). You can learn more about them in the table below. Click on a column title to sort by it.

use_subreddit = name of the subreddit used for the analysis. n_posts = number of Reddit comments containing at least 15 words. n_authors = number of unique authors. min_date = date of earliest Reddit post used (set to 6 weeks before the event). max_date = date of latest Reddit post used (set to 6 weeks after the event).

Below is a map of the mass shootings included in our analysis. Each circle is a mass shooting, and its radius is proportional to the number of people killed and the number of people injured.

What are federally declared natural disasters?

When a disaster hits a state, the governor of said state can request for a declaration by the President. The declaration paves way for the state to receive federal assistance in recovering from the disaster. For more information on how disasters get declared, check out this video from Federal Emergency Management Agency.

Which disasters did you include?

We included all 196 federally declared disasters that occurred in the same counties as the the subreddit cities that suffered mass shootings from April 2013 to March 2023. (FEMA).

In cases where a disaster was declared both as an emergency and a major disaster (these are the two declaration types), we only kept one observation.

Since each disaster declaration is filed by a state, you may see the same disaster multiples times if multiple states filed requests. You may also see that the same state filed multiple requests for the same disaster—this is because each affected county within the state gets its own request.

The disasters vary in duration. In all analyses, we used the day that the disaster began, as recorded in the FEMA dataset.

You can learn more about the disasters included in the table below. Click on a column title to sort by it.

use_subreddit = name of the subreddit used for the analysis. n_posts = number of Reddit comments containing at least 15 words. n_authors = number of unique authors. min_date = date of earliest Reddit post used (set to 6 weeks before the event). max_date = date of latest Reddit post used (set to 6 weeks after the event).

This map might help you situate the disasters. Each circle is a disaster, and its radius is proportional to the dollar amount of federal grant given to assist in recovery from the disaster.

What is Linguistic Inquiry and Word Count (LIWC)? Are there other ways to detect language trends?

LIWC is a bag-of-words approach to studying language. In a nutshell, it counts words that belong to a dictionary in a given piece of text. These dictionaries are calibrated to correspond to elusive things like cognitive processing and emotions. You can read more about it on the LIWC website. But as you know, these elusive things are, well, elusive, and the dictionaries aren’t always the best bet.

In our case, though, we have opted for LIWC because of its simplicity, which makes interpretation of the results somewhat more intuitive. Each measure (except for Analytic, which is a composite score) is literally the % of words in the given text that are in the dictionary.

There certainly are many other ways to look at language. If you think there’s an approach that suits our use case, please

So what words are in the dictionaries you used?

Here are some examples:

• We-words: we, our, us, lets
• Prosocial words: care, help, thank, please
• I-words: I, me, my, myself
• They-words: they, their, them

Check out the LIWC user manual for more details, including the frequencies of these words in corpora like Twitter and the NYT.

Not all upheavals are created equal… have you thought about how variations in the severity of the upheavals may affect your results?

We think this is a super important point! In fact, we tried to redo our analysis using some proxies for severity. The results in general look like what you would predict: more severe upheavals saw larger effects. But there are exceptions. Check them out for yourself in the plots below.

Which severity proxies did you use?

For mass shootings, we used the number of people killed + the number of people injured. For disasters, we used the total amount of federal grant given to an affected region.

These are admittedly very crude proxies. For example, the number of people affected by the disasters would be a better way to make the proxy for disasters comparable to that for the mass shootings. The proxies we used also don’t really get at how severe people think these upheavals are. We think perceived severity probably affects language use more than actual severity. It may be better captured by something like the amount of media coverage/some measure of how much people talked about the upheavals on-/off-line.

Can I look explore the language trends for each upheaval separately?

We’re working on a more user-friendly way to present this. Coming soon!

I have a better proxy/way of running this analysis in mind!

We would be so appreciative if you could share it in this feedback form!

Show me the plots!

Mass shootings

Disasters

What about time? You cover a 10-year span in your analysis. Maybe something has changed over time in how people react to these upheavals…

We think you’re totally onto something! Just anecdotally, we have found seeing distressing news every day making us less able/willing to grasp just how distressing these events actually are. Overtime we seem to be slowly but surely being desensitized to things that should have drawn out more reactions from us.

We broke down the analysis into three time bins in the plots below. This is obviously super crude, and we’d love to hear that better idea forming in your head right now: please share it in this form!

Show me the plots!

Mass shootings

Disasters

Did you run any statistical tests?

We ran a few paired t-tests (erring on the side of simplicity) to . Here’s how we did it:

• We took the mean of linguistic measures from 6 weeks to 2 weeks before the upheaval as baseline. Each author gets their own baseline.
• For every week-long period from 1 week before the upheaval to 6 weeks after the upheaval, we compared the author’s linguistic measures in that period to those in their baseline.
• If there lacks a statistically significant (we set the bar at the 3% level) and substantially meaningful (well, this is up to your interpretation—we’re again reminding you that all measures, except for Analytic, are actual % of words in the text), then we say that the person has reverted back to their baseline.
• Note: week 0 is the day of the upheaval.

There’re certainly better ways to do it! Please do share your thoughts in this form.

Shootings

Characteristic	-1 wk, N = 26,9621	baseline, N = 26,9621	Difference2	95% CI2,3	p-value2
we	0.54 (1.37)	0.52 (1.09)	0.02	0.00, 0.04	0.031
prosocial	0.47 (1.18)	0.46 (0.96)	0.01	-0.01, 0.03	0.2
i	3.57 (3.48)	3.62 (3.01)	-0.05	-0.10, 0.00	0.042
they	1.37 (2.09)	1.40 (1.75)	-0.02	-0.05, 0.01	0.2
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	0 wk, N = 25,6711	baseline, N = 25,6711	Difference2	95% CI2,3	p-value2
we	0.56 (1.38)	0.52 (1.12)	0.04	0.02, 0.06	<0.001
prosocial	0.45 (1.15)	0.45 (0.98)	0.00	-0.02, 0.02	0.9
i	3.54 (3.49)	3.58 (2.97)	-0.05	-0.10, 0.00	0.068
they	1.39 (2.11)	1.40 (1.73)	-0.02	-0.05, 0.02	0.3
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	1 wk, N = 25,8941	baseline, N = 25,8941	Difference2	95% CI2,3	p-value2
we	0.57 (1.38)	0.53 (1.11)	0.05	0.02, 0.07	<0.001
prosocial	0.49 (1.19)	0.46 (0.98)	0.04	0.02, 0.05	<0.001
i	3.51 (3.44)	3.61 (3.04)	-0.10	-0.15, -0.05	<0.001
they	1.37 (2.03)	1.39 (1.76)	-0.02	-0.05, 0.01	0.2
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	2 wk, N = 23,9331	baseline, N = 23,9331	Difference2	95% CI2,3	p-value2
we	0.52 (1.32)	0.52 (1.09)	0.00	-0.02, 0.02	0.7
prosocial	0.46 (1.19)	0.46 (1.00)	0.00	-0.02, 0.02	0.9
i	3.61 (3.49)	3.61 (3.01)	0.01	-0.05, 0.06	0.8
they	1.44 (2.14)	1.38 (1.72)	0.06	0.03, 0.09	<0.001
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	3 wk, N = 23,0131	baseline, N = 23,0131	Difference2	95% CI2,3	p-value2
we	0.54 (1.36)	0.52 (1.10)	0.01	-0.01, 0.03	0.3
prosocial	0.46 (1.16)	0.45 (0.96)	0.01	-0.01, 0.03	0.2
i	3.57 (3.47)	3.60 (2.99)	-0.03	-0.08, 0.02	0.3
they	1.44 (2.15)	1.39 (1.76)	0.05	0.02, 0.09	0.002
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	4 wk, N = 22,2311	baseline, N = 22,2311	Difference2	95% CI2,3	p-value2
we	0.51 (1.29)	0.52 (1.09)	-0.01	-0.03, 0.01	0.4
prosocial	0.46 (1.15)	0.46 (1.05)	0.00	-0.02, 0.02	0.9
i	3.52 (3.45)	3.62 (3.02)	-0.10	-0.15, -0.04	<0.001
they	1.43 (2.12)	1.39 (1.73)	0.04	0.01, 0.08	0.025
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	5 wk, N = 21,9921	baseline, N = 21,9921	Difference2	95% CI2,3	p-value2
we	0.55 (1.38)	0.50 (1.07)	0.04	0.02, 0.06	<0.001
prosocial	0.45 (1.13)	0.44 (0.96)	0.00	-0.02, 0.02	0.8
i	3.57 (3.53)	3.62 (3.00)	-0.04	-0.10, 0.01	0.2
they	1.39 (2.12)	1.41 (1.76)	-0.02	-0.05, 0.02	0.4
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	6 wk, N = 19,9011	baseline, N = 19,9011	Difference2	95% CI2,3	p-value2
we	0.53 (1.37)	0.51 (1.05)	0.03	0.00, 0.05	0.027
prosocial	0.44 (1.15)	0.45 (0.98)	-0.01	-0.03, 0.01	0.3
i	3.6 (3.5)	3.6 (3.0)	0.00	-0.06, 0.06	0.9
they	1.40 (2.09)	1.38 (1.72)	0.02	-0.02, 0.06	0.3
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Shootings - Zooming in on Week 1

Characteristic	Day 0, N = 7,0121	baseline, N = 7,0121	Difference2	95% CI2,3	p-value2
we	0.61 (1.51)	0.52 (1.03)	0.08	0.04, 0.12	<0.001
prosocial	0.53 (1.35)	0.44 (0.83)	0.09	0.06, 0.13	<0.001
i	3.20 (3.50)	3.46 (2.75)	-0.27	-0.36, -0.17	<0.001
they	1.46 (2.31)	1.41 (1.59)	0.05	-0.02, 0.11	0.14
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	Day 1, N = 7,3091	baseline, N = 7,3091	Difference2	95% CI2,3	p-value2
we	0.64 (1.50)	0.52 (1.04)	0.12	0.08, 0.16	<0.001
prosocial	0.52 (1.33)	0.47 (0.93)	0.05	0.02, 0.09	0.005
i	3.29 (3.55)	3.47 (2.81)	-0.18	-0.28, -0.09	<0.001
they	1.31 (2.08)	1.43 (1.65)	-0.12	-0.18, -0.06	<0.001
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	Day 2, N = 6,5481	baseline, N = 6,5481	Difference2	95% CI2,3	p-value2
we	0.59 (1.50)	0.54 (1.07)	0.04	0.00, 0.09	0.050
prosocial	0.54 (1.37)	0.46 (0.88)	0.08	0.04, 0.12	<0.001
i	3.39 (3.64)	3.52 (2.80)	-0.13	-0.23, -0.03	0.013
they	1.36 (2.19)	1.41 (1.61)	-0.05	-0.11, 0.02	0.14
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	Day 3, N = 6,3811	baseline, N = 6,3811	Difference2	95% CI2,3	p-value2
we	0.57 (1.50)	0.53 (1.04)	0.04	0.00, 0.08	0.062
prosocial	0.50 (1.28)	0.46 (0.87)	0.04	0.00, 0.08	0.033
i	3.36 (3.60)	3.53 (2.86)	-0.17	-0.27, -0.07	0.001
they	1.39 (2.31)	1.40 (1.58)	-0.02	-0.08, 0.05	0.7
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	Day 4, N = 6,6901	baseline, N = 6,6901	Difference2	95% CI2,3	p-value2
we	0.57 (1.49)	0.53 (0.98)	0.05	0.00, 0.09	0.029
prosocial	0.48 (1.30)	0.45 (0.89)	0.03	-0.01, 0.07	0.12
i	3.4 (3.7)	3.5 (2.8)	-0.08	-0.18, 0.02	0.13
they	1.45 (2.32)	1.38 (1.56)	0.06	0.00, 0.13	0.057
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	Day 5, N = 6,0551	baseline, N = 6,0551	Difference2	95% CI2,3	p-value2
we	0.56 (1.49)	0.53 (1.03)	0.03	-0.01, 0.07	0.2
prosocial	0.47 (1.27)	0.43 (0.84)	0.04	0.00, 0.07	0.070
i	3.47 (3.66)	3.49 (2.77)	-0.01	-0.12, 0.09	0.8
they	1.33 (2.26)	1.39 (1.56)	-0.05	-0.12, 0.02	0.13
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	Day 6, N = 5,6961	baseline, N = 5,6961	Difference2	95% CI2,3	p-value2
we	0.53 (1.38)	0.52 (1.00)	0.00	-0.04, 0.04	0.9
prosocial	0.44 (1.18)	0.46 (0.92)	-0.02	-0.06, 0.02	0.4
i	3.46 (3.66)	3.56 (2.75)	-0.10	-0.21, 0.01	0.071
they	1.35 (2.24)	1.38 (1.61)	-0.03	-0.10, 0.04	0.5
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	Day 7, N = 5,6311	baseline, N = 5,6311	Difference2	95% CI2,3	p-value2
we	0.50 (1.47)	0.50 (0.94)	0.00	-0.04, 0.04	0.9
prosocial	0.47 (1.27)	0.44 (0.90)	0.03	-0.01, 0.07	0.2
i	3.6 (3.8)	3.6 (2.8)	0.06	-0.06, 0.17	0.3
they	1.48 (2.31)	1.37 (1.58)	0.11	0.04, 0.18	0.002
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Disasters

Characteristic	-1 wk, N = 81,0611	baseline, N = 81,0611	Difference2	95% CI2,3	p-value2
we	0.54 (1.32)	0.55 (1.12)	-0.01	-0.03, 0.00	0.018
prosocial	0.47 (1.16)	0.47 (0.98)	0.00	-0.01, 0.01	0.9
i	3.59 (3.52)	3.59 (2.98)	0.00	-0.03, 0.02	0.8
they	1.42 (2.15)	1.41 (1.74)	0.00	-0.02, 0.02	0.8
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	0 wk, N = 81,0861	baseline, N = 81,0861	Difference2	95% CI2,3	p-value2
we	0.57 (1.37)	0.54 (1.12)	0.02	0.01, 0.03	<0.001
prosocial	0.46 (1.14)	0.46 (0.95)	0.00	-0.01, 0.01	0.3
i	3.6 (3.5)	3.6 (3.0)	0.02	-0.01, 0.05	0.14
they	1.40 (2.12)	1.40 (1.73)	-0.01	-0.03, 0.01	0.3
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	1 wk, N = 87,2931	baseline, N = 87,2931	Difference2	95% CI2,3	p-value2
we	0.64 (1.46)	0.55 (1.16)	0.09	0.08, 0.10	<0.001
prosocial	0.47 (1.17)	0.47 (1.00)	0.00	-0.01, 0.01	0.7
i	3.6 (3.5)	3.6 (3.1)	-0.02	-0.05, 0.01	0.2
they	1.34 (2.05)	1.41 (1.77)	-0.06	-0.08, -0.05	<0.001
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	2 wk, N = 78,9051	baseline, N = 78,9051	Difference2	95% CI2,3	p-value2
we	0.57 (1.38)	0.55 (1.14)	0.02	0.01, 0.04	<0.001
prosocial	0.47 (1.17)	0.46 (0.96)	0.01	0.00, 0.02	0.063
i	3.57 (3.50)	3.59 (3.03)	-0.03	-0.06, 0.00	0.071
they	1.41 (2.10)	1.41 (1.77)	0.00	-0.02, 0.01	0.6
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	3 wk, N = 74,5211	baseline, N = 74,5211	Difference2	95% CI2,3	p-value2
we	0.55 (1.36)	0.54 (1.12)	0.01	0.00, 0.03	0.028
prosocial	0.45 (1.15)	0.46 (0.97)	-0.01	-0.02, 0.00	0.2
i	3.6 (3.5)	3.6 (3.0)	0.00	-0.03, 0.03	0.9
they	1.42 (2.12)	1.41 (1.75)	0.01	-0.01, 0.03	0.5
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	4 wk, N = 72,8871	baseline, N = 72,8871	Difference2	95% CI2,3	p-value2
we	0.54 (1.32)	0.54 (1.12)	0.00	-0.02, 0.01	0.6
prosocial	0.45 (1.14)	0.45 (0.95)	0.00	-0.01, 0.01	0.9
i	3.57 (3.49)	3.58 (3.00)	-0.01	-0.04, 0.02	0.6
they	1.41 (2.12)	1.42 (1.75)	0.00	-0.02, 0.02	0.8
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	5 wk, N = 72,7991	baseline, N = 72,7991	Difference2	95% CI2,3	p-value2
we	0.55 (1.36)	0.54 (1.12)	0.01	0.00, 0.03	0.021
prosocial	0.46 (1.16)	0.45 (0.95)	0.01	0.00, 0.02	0.078
i	3.54 (3.48)	3.56 (3.00)	-0.03	-0.06, 0.00	0.091
they	1.42 (2.12)	1.42 (1.74)	0.01	-0.01, 0.02	0.6
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	6 wk, N = 64,1561	baseline, N = 64,1561	Difference2	95% CI2,3	p-value2
we	0.55 (1.36)	0.54 (1.11)	0.01	0.00, 0.02	0.2
prosocial	0.45 (1.13)	0.45 (0.94)	0.00	-0.01, 0.01	0.8
i	3.56 (3.51)	3.56 (2.98)	0.00	-0.03, 0.04	0.8
they	1.43 (2.14)	1.41 (1.71)	0.02	0.00, 0.04	0.035
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Disasters - Zooming in on Week 1

Characteristic	Day 0, N = 21,8351	baseline, N = 21,8351	Difference2	95% CI2,3	p-value2
we	0.61 (1.50)	0.54 (1.02)	0.07	0.04, 0.09	<0.001
prosocial	0.46 (1.25)	0.47 (0.90)	0.00	-0.02, 0.02	0.9
i	3.6 (3.8)	3.6 (2.8)	0.04	-0.01, 0.10	0.12
they	1.32 (2.19)	1.38 (1.54)	-0.06	-0.09, -0.02	0.001
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	Day 1, N = 23,2231	baseline, N = 23,2231	Difference2	95% CI2,3	p-value2
we	0.63 (1.57)	0.55 (1.06)	0.08	0.05, 0.10	<0.001
prosocial	0.47 (1.25)	0.46 (0.90)	0.01	-0.01, 0.03	0.5
i	3.6 (3.8)	3.6 (2.9)	0.01	-0.05, 0.06	0.8
they	1.28 (2.18)	1.40 (1.61)	-0.12	-0.15, -0.09	<0.001
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	Day 2, N = 23,5361	baseline, N = 23,5361	Difference2	95% CI2,3	p-value2
we	0.66 (1.61)	0.55 (1.06)	0.11	0.08, 0.13	<0.001
prosocial	0.47 (1.27)	0.46 (0.91)	0.00	-0.02, 0.02	0.9
i	3.6 (3.8)	3.6 (2.9)	0.00	-0.06, 0.05	0.9
they	1.28 (2.17)	1.40 (1.60)	-0.11	-0.15, -0.08	<0.001
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	Day 3, N = 21,7531	baseline, N = 21,7531	Difference2	95% CI2,3	p-value2
we	0.66 (1.61)	0.56 (1.06)	0.10	0.08, 0.13	<0.001
prosocial	0.46 (1.26)	0.47 (0.91)	-0.01	-0.03, 0.01	0.4
i	3.6 (3.8)	3.6 (2.9)	-0.02	-0.08, 0.03	0.4
they	1.37 (2.27)	1.42 (1.63)	-0.05	-0.09, -0.01	0.007
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	Day 4, N = 22,8421	baseline, N = 22,8421	Difference2	95% CI2,3	p-value2
we	0.65 (1.56)	0.55 (1.05)	0.10	0.07, 0.12	<0.001
prosocial	0.48 (1.29)	0.47 (0.93)	0.01	-0.01, 0.03	0.4
i	3.6 (3.7)	3.6 (2.9)	-0.05	-0.11, 0.00	0.051
they	1.36 (2.25)	1.44 (1.65)	-0.08	-0.11, -0.04	<0.001
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	Day 5, N = 22,5831	baseline, N = 22,5831	Difference2	95% CI2,3	p-value2
we	0.69 (1.66)	0.54 (1.05)	0.14	0.12, 0.17	<0.001
prosocial	0.48 (1.28)	0.46 (0.91)	0.02	0.00, 0.04	0.027
i	3.6 (3.7)	3.6 (2.9)	-0.03	-0.08, 0.03	0.3
they	1.38 (2.25)	1.42 (1.63)	-0.04	-0.08, 0.00	0.030
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	Day 6, N = 22,1491	baseline, N = 22,1491	Difference2	95% CI2,3	p-value2
we	0.65 (1.60)	0.54 (1.02)	0.12	0.09, 0.14	<0.001
prosocial	0.48 (1.27)	0.47 (0.94)	0.01	-0.01, 0.03	0.5
i	3.6 (3.8)	3.6 (2.9)	0.02	-0.04, 0.08	0.5
they	1.36 (2.22)	1.40 (1.58)	-0.04	-0.08, -0.01	0.014
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Characteristic	Day 7, N = 21,3481	baseline, N = 21,3481	Difference2	95% CI2,3	p-value2
we	0.61 (1.52)	0.55 (1.05)	0.06	0.04, 0.08	<0.001
prosocial	0.49 (1.29)	0.47 (0.89)	0.02	0.00, 0.04	0.029
i	3.6 (3.7)	3.6 (2.9)	-0.01	-0.06, 0.05	0.8
they	1.36 (2.23)	1.40 (1.58)	-0.04	-0.08, -0.01	0.020
1 Mean (SD)
2 Paired t-test
3 CI = Confidence Interval

Comparing shootings and disasters

Here we used two-sample t-tests. [1] “baseline:”

Characteristic	disaster, N = 403,5991	shooting, N = 124,9471	Difference2	95% CI2,3	p-value2
we	0.55 (1.41)	0.54 (1.39)	0.02	0.01, 0.03	<0.001
prosocial	0.53 (1.29)	0.52 (1.29)	0.00	0.00, 0.01	0.4
i	3.8 (3.7)	3.9 (3.7)	-0.07	-0.09, -0.04	<0.001
they	1.36 (2.12)	1.35 (2.11)	0.01	0.00, 0.02	0.12
1 Mean (SD)
2 Welch Two Sample t-test
3 CI = Confidence Interval

[1] “-1 wk:”

Characteristic	disaster, N = 144,3881	shooting, N = 48,0141	Difference2	95% CI2,3	p-value2
we	0.54 (1.42)	0.55 (1.47)	0.00	-0.02, 0.01	0.5
prosocial	0.51 (1.31)	0.51 (1.30)	0.00	-0.01, 0.02	0.5
i	3.8 (3.8)	3.7 (3.7)	0.02	-0.02, 0.05	0.4
they	1.37 (2.23)	1.33 (2.18)	0.03	0.01, 0.06	0.003
1 Mean (SD)
2 Welch Two Sample t-test
3 CI = Confidence Interval

[1] “0 wk:”

Characteristic	disaster, N = 156,4501	shooting, N = 49,7991	Difference2	95% CI2,3	p-value2
we	0.57 (1.48)	0.56 (1.46)	0.01	0.00, 0.03	0.12
prosocial	0.51 (1.31)	0.51 (1.30)	0.00	-0.01, 0.02	0.7
i	3.8 (3.8)	3.7 (3.7)	0.07	0.04, 0.11	<0.001
they	1.35 (2.22)	1.34 (2.20)	0.01	-0.01, 0.03	0.4
1 Mean (SD)
2 Welch Two Sample t-test
3 CI = Confidence Interval

[1] “1 wk:”

Characteristic	disaster, N = 202,8351	shooting, N = 57,6051	Difference2	95% CI2,3	p-value2
we	0.69 (1.64)	0.59 (1.49)	0.10	0.08, 0.11	<0.001
prosocial	0.55 (1.40)	0.58 (1.44)	-0.02	-0.04, -0.01	<0.001
i	3.8 (3.8)	3.6 (3.7)	0.18	0.14, 0.21	<0.001
they	1.28 (2.16)	1.34 (2.18)	-0.06	-0.08, -0.04	<0.001
1 Mean (SD)
2 Welch Two Sample t-test
3 CI = Confidence Interval

[1] “2 wk:”

Characteristic	disaster, N = 164,8861	shooting, N = 47,8031	Difference2	95% CI2,3	p-value2
we	0.59 (1.52)	0.53 (1.42)	0.06	0.04, 0.07	<0.001
prosocial	0.54 (1.35)	0.51 (1.34)	0.02	0.01, 0.04	0.001
i	3.7 (3.8)	3.8 (3.8)	-0.07	-0.10, -0.03	<0.001
they	1.36 (2.21)	1.39 (2.22)	-0.03	-0.05, 0.00	0.027
1 Mean (SD)
2 Welch Two Sample t-test
3 CI = Confidence Interval

[1] “3 wk:”

Characteristic	disaster, N = 152,5291	shooting, N = 46,7061	Difference2	95% CI2,3	p-value2
we	0.56 (1.48)	0.55 (1.47)	0.01	0.00, 0.03	0.13
prosocial	0.51 (1.31)	0.50 (1.29)	0.00	-0.01, 0.02	0.5
i	3.8 (3.8)	3.8 (3.8)	0.01	-0.03, 0.05	0.5
they	1.37 (2.22)	1.40 (2.23)	-0.03	-0.05, 0.00	0.024
1 Mean (SD)
2 Welch Two Sample t-test
3 CI = Confidence Interval

[1] “4 wk:”

Characteristic	disaster, N = 150,8591	shooting, N = 45,5741	Difference2	95% CI2,3	p-value2
we	0.55 (1.45)	0.52 (1.40)	0.03	0.02, 0.05	<0.001
prosocial	0.51 (1.29)	0.51 (1.32)	-0.01	-0.02, 0.01	0.4
i	3.8 (3.8)	3.7 (3.7)	0.01	-0.03, 0.05	0.5
they	1.38 (2.23)	1.39 (2.23)	-0.01	-0.03, 0.01	0.4
1 Mean (SD)
2 Welch Two Sample t-test
3 CI = Confidence Interval

[1] “5 wk:”

Characteristic	disaster, N = 151,4001	shooting, N = 46,3321	Difference2	95% CI2,3	p-value2
we	0.56 (1.47)	0.55 (1.46)	0.01	-0.01, 0.02	0.2
prosocial	0.51 (1.32)	0.51 (1.33)	0.00	-0.01, 0.02	0.6
i	3.7 (3.8)	3.8 (3.8)	-0.05	-0.09, -0.01	0.008
they	1.37 (2.22)	1.35 (2.22)	0.02	0.00, 0.05	0.044
1 Mean (SD)
2 Welch Two Sample t-test
3 CI = Confidence Interval

[1] “6 wk:”

Characteristic	disaster, N = 133,3751	shooting, N = 42,0801	Difference2	95% CI2,3	p-value2
we	0.56 (1.45)	0.54 (1.48)	0.01	0.00, 0.03	0.2
prosocial	0.51 (1.31)	0.50 (1.32)	0.01	-0.01, 0.02	0.3
i	3.7 (3.8)	3.8 (3.8)	-0.06	-0.10, -0.02	0.004
they	1.38 (2.24)	1.36 (2.20)	0.02	0.00, 0.04	0.11
1 Mean (SD)
2 Welch Two Sample t-test
3 CI = Confidence Interval

Can I see the graphs on your poster again?

Absolutely!

Can I see trends in other linguistic markers?

Absolutely!